AI-Driven Modeling of Socio-Economic Dynamical Systems: Operator-Centric Intelligence and Computational Delegation

This paper proposes a unified theoretical framework for AI-driven socio-economic modeling as high-dimensional complex dynamical systems. Its core thesis: all AI systems are universal approximators and controllers for such systems, with the same mathematical principles applicable to socio-economic modeling. Building on continuous flow operator (CFO), we propose Operator-Centric Intelligence (OCI), a dual-engine adaptive architecture beyond the Turing computability boundary. Instead of perfectly replicating physical reality digitally, OCI treats the real system as a natural hypercomputation oracle: it adaptively triggers computational delegation when epistemic uncertainty exceeds a threshold, injecting policy interventions and feeding observations back to the local operator. We prove the Extensional Alignment Theorem: macro-level functional agreement with physical reality suffices for valid modeling, obviating perfect micro-level replication. We compare six leading AI approaches and integrate them into the OCI-MM-MAWM architecture with a dual master operator design (World Master Operator for continuous environmental evolution, Decision Master Operator for functional policy optimization), and further introduce the Functional Memory Bank paradigm storing trajectory data as Sobolev-regularized continuous functional triples to replace traditional static data lakes. We analyze five core dimensions—algorithms, data, parameters, computing power, and energy—with quantitative cross-scale resource estimates, and rigorously examine core feasibility boundaries including the spectral truncation bound and thermodynamic Pareto efficiency of computational delegation, arguing that fuzzy correctness outperforms precise error, and probabilistic forecasting of massive trajectories offers greater feasibility and value than deterministic single-path prediction. Finally, we propose a three-stage evolutionary roadmap from proof-of-concept toy models to full socio-economic digital twins. While perfectly accurate simulation is theoretically impossible, OCI dual-engine models already deliver valuable probabilistic predictions for decision support. The field’s future lies not in pure digital determinism, but in adaptive human-AI-physics collaboration via computational delegation.